Et Tu, MacBook? Unprivileged Keystroke Inference via Built-In IMU Side Channel

Researchers reported in an arXiv paper submitted on 18 Sep 2026 that Apple MacBooks' built-in inertial measurement unit (IMU) can be read without root access via an IOKit driver, enabling a side-channel attack they call BRUTUS that recovers typed characters with 89.1% to 97.5% accuracy. Aided by language models, BRUTUS reconstructed certain sentences with 100% accuracy and also profiled users and desk environments without labels, using the IMU plus the HIDIdleTime and CGEventSource metadata interfaces. The authors conclude that access to built-in IMU sensors needs strict regulation.

Computer Science Cryptography and Security Submitted on 18 Sep 2026 Title:Et Tu, MacBook? Unprivileged Keystroke Inference and Context Profiling via the Built-in IMU Side Channel View PDF https://arxiv.org/pdf/2609.21569 HTML experimental https://arxiv.org/html/2609.21569v1 Abstract:Recent generations of Apple MacBooks embed an inertial measurement unit IMU within their unibody chassis for device orientation and motion sensing. However, this IMU inadvertently captures not only intended device-level information but also subtle physical vibrations from user interactions and the surrounding environment. These signals establish a novel, previously unexplored side channel. We uncover a vulnerability allowing non-root access to IMU data via an IOKit driver, alongside two content-free system metadata interfaces HIDIdleTime and CGEventSource that further enrich the side-channel leakage. Through rigorous characterization of the IMU data, we reveal that the leakage spans three core dimensions: 1 keystroke identity which key is typed , 2 desk surface where the laptop is placed , and 3 user behavior who is typing . Leveraging these findings, we introduce BRUTUS, the first comprehensive unprivileged side-channel attack targeting built-in IMU sensors on Apple MacBooks. BRUTUS achieves a character-level accuracy of 89.1% to 97.5% in key recovery. Furthermore, aided by language models, it can successfully reconstruct certain sentences with 100% accuracy. For user identification and environment profiling, BRUTUS correctly discovers user and environment profiles without labels and correctly assigns subsequent segments to their corresponding profiles. Ultimately, this work highlights the urgent necessity of strictly regulating access to built-in IMU sensors. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .